Instructions to use FINAL-Bench/Aether-7B-5Attn with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FINAL-Bench/Aether-7B-5Attn with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="FINAL-Bench/Aether-7B-5Attn", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("FINAL-Bench/Aether-7B-5Attn", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use FINAL-Bench/Aether-7B-5Attn with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FINAL-Bench/Aether-7B-5Attn"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FINAL-Bench/Aether-7B-5Attn",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FINAL-Bench/Aether-7B-5Attn

SGLang

How to use FINAL-Bench/Aether-7B-5Attn with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "FINAL-Bench/Aether-7B-5Attn" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FINAL-Bench/Aether-7B-5Attn",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "FINAL-Bench/Aether-7B-5Attn" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FINAL-Bench/Aether-7B-5Attn",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use FINAL-Bench/Aether-7B-5Attn with Docker Model Runner:
```
docker model run hf.co/FINAL-Bench/Aether-7B-5Attn
```

SeaWolf-AI commited on 12 days ago

Commit

d1d286f

verified ·

1 Parent(s): 33dde0e

sanitize: internal paths/ids/comments

Browse files

Files changed (1) hide show

training/run_s2.sh +2 -2

training/run_s2.sh CHANGED Viewed

@@ -20,7 +20,7 @@ nohup torchrun \
   --master_addr=<MASTER_ADDR> \
   --master_port=29500 \
   --node_rank=1 \
-  /NHNHOME/WORKSPACE/0426030036_A/aether_1b_7attn_v3/scripts/launcher_v2b_multi.py >> /NHNHOME/WORKSPACE/0426030036_A/aether_1b_7attn_v3/logs/launcher_v2b_multi.log 2>&1 &
 PID=$!
 echo "[S2] torchrun PID=$PID"
-echo $PID > /NHNHOME/WORKSPACE/0426030036_A/aether_1b_7attn_v3/logs/v2b_multi_s2.pid

   --master_addr=<MASTER_ADDR> \
   --master_port=29500 \
   --node_rank=1 \
+  /workspace/aether/scripts/launcher_v2b_multi.py >> /workspace/aether/logs/launcher_v2b_multi.log 2>&1 &
 PID=$!
 echo "[S2] torchrun PID=$PID"
+echo $PID > /workspace/aether/logs/v2b_multi_s2.pid